With miniaturization and power consumption lowering of each sensor, and development of processing technique of a sensor signal, a situational application to supply a suitable service based on an object's context (For example, location information of the object) is taken notice. As the situational application, for example, an application for special use to supply a patrolman in a factory with an operation-support, or an application for general use to supply a user having a cellular-phone with a daily life-support (For example, distribution of weather forecast), are considered. In order to improve a quality of the situational application, it is important to exactly recognize an object's context.
As one method for acquiring the object's location information, GPS is well known. However, when the object goes into a building, location-measurement by GPS is difficult. For example, in order to improve a quality of the application to supply the patrolman with the operation support, it is desired to utilize location information of the object (a terminal carried with the patrolman) indoors (in the factory).
Accordingly, as a method for acquiring the object's location information indoors, location-measurement based on a beacon (radio, ultrasonic) transmitted from an equipment (set indoors) or an intensity of radio electric wave, can be considered. However, as to location-measurement based on the beacon or the intensity of radio electric wave, an influence of shielding and reflection is large, and a cost to install the equipment increases. Furthermore, a method for acquiring the object's location information indoors using RFID technique can be also considered. However, as to a passive type RFID, a communicatable distance is relatively short, and a user's active operation is necessary to acquire the object's location information. On the other hand, as to an active type RFID, influence of a dead angle and a multi-path is large, and a cost to install the equipment more increases in comparison with the passive type RFID.
Furthermore, a method for recognizing the object's context using sound information is also proposed. Concretely, sound information around the object is acquired using a microphone. By comparing a feature quantity of the sound information with a feature quantity corresponding to a specified context, the object's context is estimated. This technique is disclosed in JP-A 2002-323371 (Kokai), V. Peltonen et al., “Computational Auditory Scene Recognition”, Proc. of ICASSP2002, pp. 1941-1944, 2002, and C. Clavel et al., “Events Detection for An Audio-Based Surveillance System”, Proc. of ICME2005, pp. 1306-1309, 2005.
In above-mentioned recognition of the object's context based on sound information, the sound information need be analyzed to compare the feature quantity. In this case, how to determine the sound information as an analysis object is a problem. In a conventional technology, a method for indicating the analysis object by the user's active operation (such as a button operation), and a method for constantly analyzing (Briefly, all sound information is indicated as the analysis object), are selectively used.
As to the method for indicating the analysis object by the user's active operation, it is effective to save a calculation quantity (for analysis processing) and a power consumption. However, its operability has a problem. On the other hand, as to the method for constantly analyzing, the user's burden for operation is small. However, the calculation quantity and the power consumption are large, which is a special problem in case that the object is a handheld mobile computer not having the calculation quantity and the power consumption to spare. Furthermore, in a period when the object is moving, a noise accompanied with the moving is apt to be mixed into the sound information. Accordingly, it is feared that accuracy of context recognition drops.